Semantic Segmentation of Remote Sensing Images With Inconsistent Resolutions via a Spectral-Geometric Iterative Fusion Network

The fusion of optical, hyperspectral, and Synthetic Aperture Radar (SAR) images is essential for semantic segmentation in remote sensing, enabling more comprehensive land cover classification through multimodal data integration. However, disparities in spatial resolution and imaging characteristics across modalities impede effective feature alignment and fusion, degrading segmentation performance. To address this problem, we propose a novel Spectral-Geometric Iterative Fusion Network (SGIFN), specifically designed to handle multimodal semantic segmentation with inconsistent resolutions. The core innovation of SGIFN lies in its unified architecture that progressively aligns, integrates, and enhances multimodal features through three newly designed modules. The Spectral-Spatial Iterative Decoupling (SSID) module introduces a novel iterative mechanism to adaptively align and decouple optical and hyperspectral features. The Spectral-Geometric Synergistic Conditional Random Field (SGS-CRF) module captures both local and long-range spatial dependencies by synergizing geometric (SAR) and spectral information. The Class-Guided Multiscale Contrastive Aggregation (CG-MCA) module further strengthens feature representation across scales via multi-class, contrastive learning. We constructed a new multimodal remote sensing dataset comprising optical, hyperspectral, and SAR images with varying resolutions collected from the Wuhan and Suzhou regions. Experimental results show that SGIFN achieves an mIoU of 69.61% on the Suzhou dataset and 63.96% on the Wuhan dataset. These results demonstrate the effectiveness of SGIFN in handling multimodal data with inconsistent resolutions.